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Automated Chicken Counting in Surveillance Camera Environments Based on the Point Supervision Algorithm: LC-DenseFCN
The density of a chicken population has a great influence on the health and growth of the chickens. For free-range chicken producers, an appropriate population density can increase their economic benefit and be utilized for estimating the economic value of the flock. However, it is very difficult to...
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Published in: | Agriculture (Basel) 2021-06, Vol.11 (6), p.493 |
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creator | Cao, Liangben Xiao, Zihan Liao, Xianghui Yao, Yuanzhou Wu, Kangjie Mu, Jiong Li, Jun Pu, Haibo |
description | The density of a chicken population has a great influence on the health and growth of the chickens. For free-range chicken producers, an appropriate population density can increase their economic benefit and be utilized for estimating the economic value of the flock. However, it is very difficult to calculate the density of chickens quickly and accurately because of the complicated environmental background and the dynamic number of chickens. Therefore, we propose an automated method for quickly and accurately counting the number of chickens on a chicken farm, rather than doing so manually. The contributions of this paper are twofold: (1) we innovatively designed a full convolutional network—DenseFCN—and counted the chickens in an image using the method of point supervision, which achieved an accuracy of 93.84% and 9.27 frames per second (FPS); (2) the point supervision method was used to detect the density of chickens. Compared with the current mainstream object detection method, the higher effectiveness of this method was proven. From the performance evaluation of the algorithm, the proposed method is practical for measuring the density statistics of chickens in a farm environment and provides a new feasible tool for the density estimation of farm poultry breeding. |
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From the performance evaluation of the algorithm, the proposed method is practical for measuring the density statistics of chickens in a farm environment and provides a new feasible tool for the density estimation of farm poultry breeding.</description><identifier>ISSN: 2077-0472</identifier><identifier>EISSN: 2077-0472</identifier><identifier>DOI: 10.3390/agriculture11060493</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Agriculture ; Algorithms ; aquaculture automation ; Automation ; Cameras ; chicken detection ; Chickens ; computer vision ; Deep learning ; Environmental statistics ; Farms ; Frames per second ; Livestock breeding ; Localization ; Neural networks ; Object recognition ; Performance evaluation ; Population density ; Poultry ; Poultry farming ; Semantics ; Statistical analysis ; Surveillance ; Teaching methods</subject><ispartof>Agriculture (Basel), 2021-06, Vol.11 (6), p.493</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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subjects | Accuracy Agriculture Algorithms aquaculture automation Automation Cameras chicken detection Chickens computer vision Deep learning Environmental statistics Farms Frames per second Livestock breeding Localization Neural networks Object recognition Performance evaluation Population density Poultry Poultry farming Semantics Statistical analysis Surveillance Teaching methods |
title | Automated Chicken Counting in Surveillance Camera Environments Based on the Point Supervision Algorithm: LC-DenseFCN |
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